Several previous studies reported relationships between speed of information processing as measured with the drift parameter of the diffusion model (Ratcliff, 1978) and general intelligence. Most of these studies utilized only few tasks and none of them used more complex tasks. In contrast, our study (N = 125) was based on a large battery of 18 different response time tasks that varied both in content (numeric, figural, and verbal) and complexity (fast tasks with mean RTs of ca. 600 ms vs. more complex tasks with mean RTs of ca. 3,000 ms). Structural equation models indicated a strong relationship between a domain-general drift factor and general intelligence. Beyond that, domain-specific speed of information processing factors were closely related to the respective domain scores of the intelligence test. Furthermore, speed of information processing in the more complex tasks explained additional variance in general intelligence. In addition to these theoretically relevant findings, our study also makes methodological contributions showing that there are meaningful interindividual differences in content specific drift rates and that not only fast tasks, but also more complex tasks can be modeled with the diffusion model. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability – in science in general, but also specifically in human neuroscience – have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.

Individual differences in the speed of information processing have been hypothesized to give rise to individual differences in general intelligence. Consistent with this hypothesis, reaction times (RTs) and latencies of event-related potential have been shown to be moderately associated with intelligence. These associations have been explained either in terms of individual differences in some brain-wide property such as myelination, the speed of neural oscillations, or white-matter tract integrity, or in terms of individual differences in specific processes such as the signal-to-noise ratio in evidence accumulation, executive control, or the cholinergic system. Here we show in a sample of 122 participants, who completed a battery of RT tasks at 2 laboratory sessions while an EEG was recorded, that more intelligent individuals have a higher speed of higher-order information processing that explains about 80% of the variance in general intelligence. Our results do not support the notion that individuals with higher levels of general intelligence show advantages in some brain-wide property. Instead, they suggest that more intelligent individuals benefit from a more efficient transmission of information from frontal attention and working memory processes to temporal-parietal processes of memory storage. (PsycINFO Database Record

More intelligent individuals typically show faster reaction times. However, individual differences in reaction times do not represent individual differences in a single cognitive process but in multiple cognitive processes. Thus, it is unclear whether the association between mental speed and intelligence reflects advantages in a specific cognitive process or in general processing speed. In this article, we present a neurocognitive-psychometrics account of mental speed that decomposes the relationship between mental speed and intelligence. We summarize research employing mathematical models of cognition and chronometric analyses of neural processing to identify distinct stages of information processing strongly related to intelligence differences. Evidence from both approaches suggests that the speed of higher-order processing is greater in smarter individuals, which may reflect advantages in the structural and functional organization of brain networks. Adopting a similar neurocognitive-psychometrics approach for other cognitive processes associated with intelligence (e.g., working memory or executive control) may refine our understanding of the basic cognitive processes of intelligence.